1. Field of the Invention
The present invention relates to an information processing apparatus, a processing method therefor, and a non-transitory computer-readable storage medium.
2. Description of the Related Art
There is conventionally known machine learning which analyzes a new pattern using learning patterns. There is especially known a pattern recognition method called a classification tree and decision tree, as in “Leo Breiman, Jerome Friedman, Charles J. Stone, and R. A. Olshen, “Classification and Regression Trees”, Chapman & Hall/CRC (1984) (to be referred to as literature 1 hereinafter)”. Since this method can analyze a pattern using a tree structure at high speed, it has been useful especially when the capability of a computer is low.
By considering a pattern recognition problem as a pattern identification problem, a type of pattern to be identified is referred to as “class”. The term “class” will be used in this sense hereinafter.
The classic classification tree and decision tree as described in literature 1 have a disadvantage that the recognition performance is not so high. To overcome this disadvantage, there has been proposed a method of using a set (ensemble) of classification trees as described in U.S. Pat. No. 6,009,199 (to be referred to as literature 2 hereinafter). This technique achieves higher recognition performance by creating L (L is a constant of 2 or larger, and usually falls within the range from 10 to 100) classification trees, and using all of them.
As an example of a technique in which the method of using a set (ensemble) of classification trees is applied to a computer vision, there is known a technique described in “Vincent Lepetit and Pascal Fua, “Keypoint Recognition Using Randomized Trees”, IEEE Transactions on Pattern Analysis and Machine Intelligence (2006) pp. 1465 to 1479 (to be referred to as literature 3 hereinafter)”. In this literature, an image (32×32 pixels) is considered to be a target, and a classification tree is created based on the luminance value of the image. More specifically, in each node of a classification tree, two points are randomly selected in an image having a predetermined size (32×32 pixels), and their luminance values are compared with each other. This implements branch processing. The literature has reported that it is possible to perform the processing at extremely high speed and the recognition accuracy is sufficiently high.
However, it is impossible to apply, intact, the technique described in literature 3, when the background considerably changes, for example, in the case of recognition of parts laid in a heap or human recognition in the crowd. This is because the luminance value of a background portion in an unknown image is completely different from that in an image to be learned. More specifically, a luminance value unrelated to a target object may inadvertently be used to compare the luminance values of two points in each node of a classification tree. In this case, it is only possible to obtain an unreliable result in pattern recognition when using a (conventional) classification tree. Although an attempt is made to compare the luminance values of two points in a portion where a target object exists, a portion except for the target object may often be referred to.